Heterogeneous federated contrast diagnosis framework integrating knowledge distillation and continuous learning

Abstract

Federated learning safeguards privacy by training models collaboratively on distributed data and has demonstrated substantial promise in industrial fault diagnosis. Nevertheless, non-identically distributed client data, bespoke local model architectures, and catastrophic forgetting induced by evolving task streams markedly limit diagnostic performance. To address these challenges, a heterogeneous federated contrastive diagnosis framework with knowledge distillation and continual learning (HFCDF-KDCL) is introduced. Initially, each client is tasked with learning a personalized feature extractor from its own labeled private data to capture domain-specific characteristics. Subsequently, unlabeled public data are leveraged in a contrastive learning phase that minimizes the feature distance between identical samples across clients while maximizing inter-sample disparity, thereby yielding domain-invariant and highly discriminative representations compatible with heterogeneous-model structures. Finally, a dual-stream knowledge-distillation strategy is devised: within each task, the local pre-trained and the globally aggregated models act jointly as teachers whose soft targets fuse intra- and cross-domain knowledge, whereas across tasks, earlier-task models serve as distillation teachers to constrain parameter updates and mitigate catastrophic forgetting. Extensive experiments on multiple gear and bearing fault datasets indicate that HFCDF-KDCL achieves superior diagnostic accuracy and robustness relative to the current state-of-the-art federated approaches.